-
Notifications
You must be signed in to change notification settings - Fork 17
/
STAARpipeline_Gene_Centric_Noncoding_Long_Masks.r
93 lines (78 loc) · 3.5 KB
/
STAARpipeline_Gene_Centric_Noncoding_Long_Masks.r
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
#####################################################################
# Gene-centric analysis for noncoding rare variants in long masks
# of protein-coding genes using STAARpipeline
# Xihao Li, Zilin Li
# Initiate date: 11/04/2021
# Current date: 02/17/2024
#####################################################################
rm(list=ls())
gc()
## load required packages
library(gdsfmt)
library(SeqArray)
library(SeqVarTools)
library(STAAR)
library(STAARpipeline)
###########################################################
# User Input
###########################################################
## aGDS directory
agds_dir <- get(load("/path_to_the_file/agds_dir.Rdata"))
## Null model
obj_nullmodel <- get(load("/path_to_the_file/obj_nullmodel.Rdata"))
## QC_label
QC_label <- "annotation/filter"
## variant_type
variant_type <- "SNV"
## geno_missing_imputation
geno_missing_imputation <- "mean"
## Annotation_dir
Annotation_dir <- "annotation/info/FunctionalAnnotation"
## Annotation channel
Annotation_name_catalog <- get(load("/path_to_the_file/Annotation_name_catalog.Rdata"))
# Or equivalently
# Annotation_name_catalog <- read.csv("/path_to_the_file/Annotation_name_catalog.csv")
## Use_annotation_weights
Use_annotation_weights <- TRUE
## Annotation name
Annotation_name <- c("CADD","LINSIGHT","FATHMM.XF","aPC.EpigeneticActive","aPC.EpigeneticRepressed","aPC.EpigeneticTranscription",
"aPC.Conservation","aPC.LocalDiversity","aPC.Mappability","aPC.TF","aPC.Protein")
## output path
output_path <- "/path_to_the_output_file/"
## output file name
output_file_name <- "TOPMed_F5_LDL_Noncoding"
## input array id from batch file
arrayid_longmask <- as.numeric(commandArgs(TRUE)[1])
###########################################################
# Main Function
###########################################################
## gene number in job
gene_num_in_array <- 50
group.num.allchr <- ceiling(table(genes_info[,2])/gene_num_in_array)
sum(group.num.allchr)
## analyze large noncoding masks
arrayid <- c(21,39,44,45,46,53,55,83,88,103,114,127,135,150,154,155,163,164,166,180,189,195,200,233,280,285,295,313,318,319,324,327,363,44,45,54)
sub_seq_id <- c(1009,1929,182,214,270,626,741,894,83,51,611,385,771,493,671,702,238,297,388,352,13,303,600,170,554,207,724,755,1048,319,324,44,411,195,236,677)
region_spec <- data.frame(arrayid,sub_seq_id)
sub_seq_id <- ((arrayid_longmask-1)*5+1):min(arrayid_longmask*5,length(arrayid))
results_noncoding <- c()
for(kk in sub_seq_id)
{
print(kk)
arrayid <- region_spec$arrayid[kk]
sub_id <- region_spec$sub_seq_id[kk]
chr <- which.max(arrayid <= cumsum(group.num.allchr))
## aGDS file
agds.path <- agds_dir[chr]
genofile <- seqOpen(agds.path)
genes_info_chr <- genes_info[genes_info[,2]==chr,]
gene_name <- genes_info_chr[sub_id,1]
results <- Gene_Centric_Noncoding(chr=chr,gene_name=gene_name,genofile=genofile,obj_nullmodel=obj_nullmodel,
rare_maf_cutoff=0.01,rv_num_cutoff=2,
QC_label=QC_label,variant_type=variant_type,geno_missing_imputation=geno_missing_imputation,
Annotation_dir=Annotation_dir,Annotation_name_catalog=Annotation_name_catalog,
Use_annotation_weights=Use_annotation_weights,Annotation_name=Annotation_name)
results_noncoding <- append(results_noncoding,results)
seqClose(genofile)
}
save(results_noncoding,file=paste0(output_path,output_file_name,"_",arrayid_longmask+379,".Rdata"))